LPIPS is also defined on patch level.
Given two images, it computes their similarity distance in the feature space across different spatial resolution using a pretrained AlexNet model [1]}.
The final similarity score is the average over all distance maps.
To compute mLPIPS, we follow the previous works [2]}, [3]}, [4]} and first apply the co-visibility mask on the input images by zeroing out the unseen regions.
Given the output distance maps at each spatial resolution, we then apply the same mask with downsampling and compute the masked average distance score.
It should be noted that the pretrained AlexNet has a receptive field of \(195^2\) .
Thus when the co-visibility mask is small (most of the pixels are not seen during training), this metric can be artificially low due to the zeroing operation.
